On the other hand, if all the accent phrases are predicted non-emphasized (N ), accuracy is 63.8%, then both recall and precision of emphasized accent phrases are 0%.

the proposed method offered 13.6 points higher accuracy than these above forced predictions.


Based on results, a seven-stated HMM and 16coponent Gaussian Mixtures with diagonal covariance matrices yields the best accuracy result at 84.15%.

this setting is set as the setting of the speech recognizer in our approach and also is referred as baseline.


(2000) shows that the lower bound of the last equation can be found using Jensen's inequality.

we can express the complete log-likelihood of the parameters, logL c (✓|T ), as follows: The last equation is used in each iteration to check whether or not the parameters have converged.


As we mentioned previously, we see the problem of opinion tweet extraction as a binary classification problem.

we assume that a tweet can be classified into two categories: an opinion tweet and non-opinion tweet.


It has been widely recognized that Twitter can potentially contain much useful information.

many researchers have conducted content analysis on Twitter (Java et al., 2006;Krishnamurthy et al., 2008;Pennacchiotti and Gurumurthy, 2011;Mehrotra et al., 2013).


It is difficult to straightforwardly resolve NSD because a search space of tweet pair combinations is exponentially large.

we simplify the NSD problem from the viewpoint of the text segmentation and provide an approximate solution.


If the sentence boundary sb ij between s i and s j has a lower boundary score than the threshold d th , the sentence pair is detected as belonging to the same segment; otherwise, it is not.

text segmentation in the input document is naturally done when all sentence boundaries are determined.


With the post-based window, the number of words to be included in the window varies with the length of each tweet.

we can include more meaningful context into the boundary scores.


Our stream data are much shorter than those assumed in the original TextTiling algorithm.

it was frequently observed that the number of words is less than the window size at the end of the stream when using the word-based window.


It was found that there are many URL tweets with insufficient information to detect source tweets because they are composed of very few words.

we consider enriching URL tweets with web content referred by the URL written in them.


where, sigmoid(x) = The revised polarity score may be unreliable if the frequency of the word is too low, or the difference between positive and negative tendency is not great enough.


In this method, we combine sentiment interpolation and sentiment augmentation together.

three additional augmentation features (Subsection 3.3.3) will be added to the sentiment interpolation vector (Subsection 3.3.4) as the extra features.


The result shows that when the add-on lexicon was applied, the accuracy was increased about 2% compared to applying only SWN in positive-negative classification, while only 0.25% in 3-way classification.

the add-on lexicon is more suitable for positive-negative sentiment classification than positive-neutral-negative sentiment classification.


In order to see if this hypothesis is on the right track, I conducted a corpus analysis of OSV order in Japanese, largely based on the quantitative approach.

it has been revealed that both givenness and heaviness have a high explanatory power for the usage of OSV order.


However, this description begs the question because givenness itself is not a clear-cut concept.

it is necessary to define givenness in an objective way.


In (5), both football and baseball belong to sports.

baseball is indirectly connected with football through the concept sports.


However, there is no direct relationship because baseball is not included in football.

baseball is not regarded as discourse-old information.


Note that the head of the scrambled object is nominalizer no but it is anchored by Hänsel and candy house.

the RD of the scrambled object is replaced by the anchoring expression's RD and its RD is 1.


sono-imi 'that meaning' and sono-hon 'that book'.

i 2 20+ includes the examples that have no antecedent.


Hence, they should be excluded from the heavy examples.

it is more appropriate to conclude that heaviness accounts for 71.40 % of the examples, which is the total ratio of 'real' heavy objects.


Consequently, it was revealed that there was no correlation between givenness and heaviness (r = -.05, p<.01).

rD of the scrambled object is independent of its length.


Rather, it is possible that the NP nanatsumen 'Seven Masks' is introduced to the discourse in order to make the scrambled object given information.

this type of example is not a crucial counterexample to my hypothesis.


It has been said that interlocutors are conscious of each other (Chafe, 1987: 26;1994:79).

it is not too much to say that 1 st and 2 nd persons are permanently given information.


If such a tendency is universal in OSV as a whole, it is economical to use only one concept in order to explain the usage of scrambling.

i checked the correlation coefficient between givenness and heaviness.


Consequently, it has been revealed that there is no correlation between them.

we can conclude that givenness and heaviness independently influence word order choices in Japanese.


However, using observed agreement to measure reliability does not take into account agreement that is due to chance and hence is not a good measure of reliability.

an analysis using Cohen's Kappa statistic (Cohen, 1960)    chance agreement would equate to 0.


On the contrary, the rest of tags have a much higher agreement: on average, they are used by the four annotators 63.2% of the times, by three 19.9%, by two 9.2% and by one 7.7% of times.

this tag should at most be used to filter out problematic sentences, which annotators cannot comprehend, and not for proper annotation of sentences.


Syntactic factors seem to be in between (k = 0.750 for experts and 0.523 for non-experts), although their low frequency makes the figures less reliable.

more care should be paid to pragmatic distinctions.


[...] chapter 2 is about the portuñol process and the problems that the portuñol confronts nowadays.'

to determine the acceptability of article usage, annotators should not rely only on their intuition as native speakers but also consult existing rules and recommendations published in reference dictionaries and grammars as RAE (2006) and RAE (2009).


It is not recommended to use a tag like NC, "difficult to judge", because it has the lowest reliability.

we recommend simply not annotating the noun phrase if it impossible to determine the acceptability of the article usage.


Even in other sentence mood, there are some verbs which conjugate in the same way.

there still exists a problem to select the one right subject among possible subjects for some verb endings.


The text type we focused on is a spoken Spanish, so that there are even no antecedents for some zero subjects in the text.

to make a list of forward-looking centers would be difficult in Spanish spoken texts.


Data sparseness makes the vector representations sparse (e.g., the vector of a low frequent word tends to have many zero entries), thus they do not always reliably represent the meanings of words.

the similarity of word pairs can be inaccurate.


For instance, adjectives in English precede the noun, while they typically follow the noun in French (the blue sky vs. le ciel bleu); in Modern Standard Arabic the verb precedes the subject, and in Japanese the verb comes last.

source language phrases cannot be translated and placed in the same order in the generated translation in the target language, but phrase movements have to be considered.


The word alignment results can be used to estimate lexical and phrasal translation probabilities for machine translation; alternatively they can be helpful for bilingual lexicography and computer aided translation.

it is crucial that we produce high-precision, broad coverage word alignments.


In the first learning stage, we use the initial word alignments to generate positive and negative instances for training a classifier that predicts alignment links via crosslingual relatedness.

the output of this stage is a set of (k, i, j, Pos or Neg) tuples, where Pos or Neg denotes whether (e k i , f k j ) is a valid alignment link in (E k , F k ).


For this, we use the word-to-morphme and morpheme-to-word alignments to estimate lexical translation probability.

we have feature 2 = max e, m2f k j P (e | m)sim(e, e k i ).


The transliteration feature is designed to handle hard-to-align name entities appearing only once or twice in the whole corpus.

we we have where P translit is a transliteration model trained on a list of bilingual named entities.


For instance, an English preposition (i.e., IN) tends to align with a Chinese preposition or directional postposition (i.e., P or Ng).

we have feature 4 = (pos(e k i ), pos(f k j )), where pos returns the part of speech of English word e k i or foreign word f k j in (E k , F k ).


Perceptually, since it is clearly shown that the value of accuracy rates was reversed for Cantonese and Sichuanese speakers, and the inclination was especially true for the /θ/ sound.

it could be drawn from the results that Cantonese and Sichuanese speakers of English have different perception of sound categories, and apply different assimilation routes to sounds.


It could be impossible for children to understand a sentence where the order of the words is random.

word order plays an important role in text comprehension.


In the first lesson of learning a L2WS, the first thing that comes to our mind is the 26 letters.

learning how to read and write kana and hangul are expected in the first chapter of Japanese or Korean textbooks, because they function like the 26 letters of English as the most basic building blocks in the writing system (WS, hereafter).


Comparing the repeated letters as shown in table 2, it is obvious that letters in high usage frequencies all come from Columns 3-6 in figure 1 (H3, H4, L5 and L6).

it is quite reasonable to put the three letters: (24 th ), (28 th ) and (29 th ) to later stages of the learning sequence.


Although all 19 consonant graphemes are phonemically contrastive in Korean language, the three groups of obstruent consonants may not be perceptually or consciously distinctive to many non-native speakers (Au and Cheung, 2014).

allocating these "pseudo"-homophonic obstruent consonants (e.g.


(2013b) point out that there are roughly only 26.2% ONE-TO-ONE triplets.


It defines a scoring function ||h t||, and obversely this model can not discriminate entity-pairs with different relationships.

unstructured is commonly regarded as the baseline approach.


Moreover, its lower parameter complexity implies the capability of learning the embeddings for large-scale knowledge graphs.

it is a promising model which is both effective and efficient.


(Bordes et al., 2013a;Bordes et al., 2013b) that most of the relation instances in the knowledge graph are hierarchical and irreflexive.

the relationship of each triplet (h, r, t) can be regarded as a directed transition r in the embedding space from the head entity h to the tail entity t. Ideally, if all the correct triplets follow the assumption that every relation instance is strictly single-mapping (i.e.


We would like to constrain ONE-TO-ONE instances more than MANY-TO-MANYs.

we design a formula to measure the weights as follows, The scoring function of TransM shown in Table 1 indicates that the parameter complexity of TransM 8 The detail of constructing corrupted triplet is described in (Bordes et al., 2013b).


However, both of the benchmark datasets contain much fewer relationships.

we build another dataset obeying the principle proposed by Socher et al.


However, first the MED algorithm is used to identify the closest matching Hindi word hw for a given word in the test data.

∀ word q ∈ Hindi Dictionary For a given word w in the test data, score(w) = log( freq(hw))/M hin_score(hw) = score(w) … we get English score and Hindi score for each word in the Dataset 1.


A line of work in cognitive science and linguistics shows that the discourse relation has a strong influence on pronoun interpretation (Hobbs, 1979;Kehler et al., 2008;Rohde and Kehler, 2013).

a useful example should have the same discourse relation as the source sentence.


For example, omitting negation in (6) could make the whole sentence difficult to understand.

we also keep the negation modifier of the head: Dependent of a light head A head of an actor could be a light verb, which is a verb that has little meaning on its own.


For example, "shot back" in (1b) indicates a reaction against the action of the main clause.

we also keep the particle following the head.


Note that the Google Web Search API expands some queries and returns results containing related words.

we use the synonym instead of the exact match to increase coverage.


9 We provided gold mentions (the two candidate antecedents and the target pronoun) as the inputs for each baseline system in testing.

the baseline systems did not need to perform mention detection.


non-comparative predicates) like (9), this means that semantically they do not assert degree by themselves.

the denotation of Cantonese adjectives should not include d. Also, following the general assumption DegP (see section 2), I assume that the Degree Phrase is more functional than the Adjective Phrase and thus merges later than the predicate in syntax.


It is important to note that a head analysis of gwo3 would wrongly predict ungrammatical sentences like (12).

structure 14shows that gwo3 must not be a head.


With the homomorphic approach, the need for category-specific syntaxsemantics is reduced, because the behaviors in different categories (V and Adj in our case) can be captured under the same syntax-semantics structure.

such an approach is desirable for any explanatory theories for human language.


Firstly, variations of the same token share the key words and present in similar patterns, for instance, it's true, this is true and true are all considered as variations of that's true, since they contain the same key word true with similar patterns.

the whole utterances have similar semantic meanings.


Gardner, 2004;2007), different from that's right.

right is not treated as a variant of that's right in this stage, which will be verified by the statistical information later.


As a consequence, right is not treated as a variant of that's right in this stage, which will be verified by the statistical information later.

the final list can be identified as shown in Table 1, where similar patterns take one-to-one correspondence.


By contrast, this is true is relatively consistent with that's true in primary functions they serve.

in the pattern "THAT/IT/THIS + BE + TRUE", that, it and this indicate their particular preference as well.


That's correct and its variations are used infrequently, with a total occurrence of 21 in the whole corpus.

there are far less variations in this set.


An adaptive computer-assisted language learning and teaching system can pick up news clips as authentic materials from the Internet according to learner listening proficiency if it is equipped with a listenability measuring method that takes into both linguistic features of a news clip and the listening proficiency.

we developed a method for measuring listening proficiency-based listenability.


An adaptive computer-assisted language learning and teaching system can pick up news clips as authentic materials from the Internet according to the listening proficiency if it is equipped with a listenability measuring method that takes into both linguistic features of a news clip and the listening proficiency.

we propose an automatic method that statistically measures listenability for EFL learners.


That is, listening material can be appropriate for a learner but not for another.

it is necessary to measure listenability based not only on linguistic features but also learner features.


However, learners have different proficiencies; thus, individual differences of listening proficiency should be considered.


The results showed that there was a statistically significant difference between our method and the other methods I-III (p < 0.003 after Bonferroni correction for three comparisons).

our method was marked with the highest correlation.


The sentence contains both positive ("love") and negative word ("ignored") in a sentence.

it can be classified as a sarcastic sentence.


Although the tweet contains contradictions in sentiment polarity, the two sentences are not coherent.

it should not be classified as a sarcastic tweet.


ConceptNet will be used to expand the concepts for the words whose sentiment score is unknown.

the sentiment score of the unknown words can be recognized through their generated concepts and definitions.


If all sentences are independent on each other, it is not obvious to say that the tweet is sarcastic.

we introduce a set of heuristic rules to identify coherence across multiple sentences.


As discussed earlier, contradiction in the sentiment score and coherent within multiple sentences are useful for sarcasm identification.

we introduce two new binary features, contra and contra + coher, considering contradiction of polarity and coherence in the tweet.


Usually, the higher the margin, the more reliable the output.

we take the output from the classifier with higher margin as the final result.


However, some expanded concepts may be irrelevant with the context of the tweet.

if we can obtain only the suitable concepts for each word, the performance of our method might increase.


Since Chula DAISY aimed to handle Thai book contents, the domain of the application was generally Thai well-written text.

a standard Thai G2P handler and a standard Thai synthesizer engine handler were sufficient Punyabukkana et al.


Each unit repeatedly appears and shares the same musical phrases.

rhythm and meter are common to shared among the same type of units.


For example, if the input is a mora length of the phrase M phrase i,j,k = 7, the model should first generate a sequence of the mora word lengths [m 0 , m 1 , m 2 , m 3 ] = [3, 1, 2, 1], followed by a word sequence [w 0 , w 1 , w 2 , w 3 ] = [" (a-na-ta)", " (no)", " (ka-ta)", " (ni)" (your shoulder)].

we specified that a sequence of words with the mora length m is generated with some probability P (m|M phrase i,j,k ).


In addition, research on Japanese has demonstrated that nominative case marker GA usually marks new information and topic marker WA prefers given information.

based on a self-paced reading task, we will study the relationship among word order, topic and case marker, and given-new ordering.


Note that whphrases generally require new information and are not anaphoric because they have no specific referents.

wh-phrases cannot be accompanied with thematic WA, which usually requires an anaphoric antecedent.


Whether WAmarked constituents are given or new is not crucial for contrastive topic WA.

in our experiment, participants seem to have considered topic marker WA to have a contrastive meaning in STOPOACCV and OTOPSNOMV and thus there was no difference in reaction time between the given-new condition and the new-given condition in STOPOACCV and OTOPSNOMV.


Moreover, the highest error rates mean that the superset relation for our experiment was not enough to allow topicalization.

it is unlikely that participants regarded WA-marked NPs as thematic topics because of our design.


However, there is not any shared and generally accepted definition of them.

various authors dealing with discourse studies try to give a list of connectives for the given language and to describe their common features.


'The new leadership had to find a job for 600 employees.

the managers started to look for contracts not only around the country but also abroad.'


Secondary connectives form a very heterogeneous group of connective structures that behave differently than primary connectives in some cases.

we could adopt only a part of the old principles and we had to create some new for the specific structures.


We decided that we will not include the non-universal structures in discourse connectivesbecause even though they express certain discourse relation, they contain too much other lexical items occurring in the connective structure only occasionally.

we will call these structures (like because of this increase, the reason of his late arrival is etc.)


We may see that relations of secondary connectives form 5 % of the total number.

the primary connectives may be viewed as a centre of all connecting expressions and the secondary as its periphery.


), they are mostly one-word expressions, lexically frozen.

they mostly do not allow modification (i.e.


the PDT example Další důvod je složitější a je v podstatě filozofický 'Another reason is more complex and in essence philosophical' that cannot be fully replaced by the primary connective, as we cannot say *philosophically because.

the substitution by primary connectives is very limited in these cases.


On the other hand, although they seem to be peripheral as a whole group because of their lower frequency, they enrich the discourse by various structures and behaviour the primary connectives can never do.

due to their idiosyncrasy in behaviour, they occupy a special and unique place in discourse and the term secondary does not mean less important -we established the opposition of primary and secondary connectives mainly due to their peculiarities and different behaviour.


These nonuniversal phrases are on the very edge of the connecting elements and they have very little chance to be grammaticalized.


Because Explanation is a discourse relation, it is a relation between a pair of adjacent segments π2 and π4.

it is a relation between two modal expressions, stating that "Realizing that I had a pain in my neck caused me to infer that I strained my neck," which is as expected.


While Narration was the most frequently used label, and so it was biased greatly in K&B, the frequency of each relation in our study is more balanced than that in K&B.

the classification in our study is more appropriate for performing machine learning.


The information reported in time-series documents such as news paper articles is updated day by day, and we often need a summary for the information which has so far been reported.

in this study we aim to propose an on-line summarization of time-series documents with a graph-based algorithm.


In our method, it is required to extract the sentences which have high ranking score and are not similar to the already extracted sentences as a part of a summary.

we modify MMR-MD as shown in equation (5).


476 the result, and if η is smaller, the ranking score influences the result.

even though we can find some parts where the accuracy is higher if we put weight on the penalty term rather than the ranking score term, the difference is a little.


the proposed method gets close or higher accuracy than LexRank.


I have taken the former to indicate Hon = [.6, .9] and the latter to also indicate Hon = [.6, .9].

the two together yield Hon(S ) = [.6, .9] given the formula for calculating the politeness of a sentence in (6).


Text criticism is not only looking at the lexical choices made by the writer or speaker but also at the potential meaning behind these choices.

the unit of investigation can vary in length as needed.


(Others didn't come; assertion>only) Just like English only, the dake sentence involves a focus structure and gives rise to two entailments, 'John came' and 'Other alternative individuals didn't come'.

dake generates a set of alternatives in the sense of Rooth (1985Rooth ( , 1995 and expresses that the alternative propositions are false (see also Horn, 1969): 14John-dake-ga kita = 1 iff a. John came; and where C is a contextually given set of propositions and C is the subset of the Rooth's (1992) focus value of "[John] F came", i.e., C ∈ In other words, the exhaustification by dake happens at the level of the propositional content.


Topics are analyzed as context-shifter for the subsequent updates, analogously to antecedents of conditionals in dynamic semantics.

topicalized questions have an analogous semantics to conditional questions.


If verbs with the colloquial expressions cannot be processed correctly, the machine translation result will become impossible to understand.


The effort to iron out misunderstandings is necessary.

the following conversation would be used for ironing out misunderstanding and the business correspondence would be suspended temporarily.


Refer to our idea; the system is not sure that the suspect part is untranslatable.

the system replaces the suspect expressions with the "correct" expressions.


In Case 1, the difference of similarity (0.61) can be seen as having tangibly increased the translation quality after replacing the suspect terminal expressions.

we concluded that the original colloquial expressions "わ なきゃだ" is untranslatable.


Alternatively, the difference of similarity in Case 2 (0.0) can be seen as having not changed the translation quality after replacing the suspect terminal expressions.

the original expression is translatable.


We believe that the users of instant messaging do not like the excess detection.

we tune the system to reduce the excess detection and to require higher precession more than the recall.


We also explained that the utterance unit criteria could not evaluate the effectiveness of our system.

one future direction is to create a criterion for evaluating the effectiveness of the conversation.


The returned sentences pairs can be examined for alignment accuracy, used for language learning, or passed on to sophisticated text simplification models (e.g., (Zhu et al., 2010) and (Woodsend and Lapata, 2011)).

it is crucial that the aligned sentences be accurate.


At the same time, the set of identified sentence pairs cannot be so small that it bores the user or hurts the performance of the subsequent (typically data intensive) simplification models.

our goal is to return a reasonable-sized set of parallel sentences that, at the same time, must contain correct sentence mappings of the parallel articles.


As one can speculate, TF*IDF penalizes frequent content words (e.g., "gorillas" assigned 0.1 compared to "of" assigned 0.125), but frequent content words are more likely to be key words and should receive more attention during sentence aligning.

we also turn to PageRank, a famous keyword extraction algorithm, to infer word significance and give better share of weights for essential words.


In other words, words in the original sentences will be translated or simplified in order.

word vectors contain words' relative positions in sentences, posi(w)/|Sent| where absolute word positions are divided by sentential word lengths.


Intuitively, mapping content words in sentences is more important than mapping non-content words.

step (3e) further weights sentence-level cosine similarity using the aligning ratio of content words in sentences, AlignedRatio cw, computed as 2×|CWo CWs|/(|CWo|+|CWs|) where the size of the common content words is divided by the sum of the size of the individual sentential contentword set.


red") and "ran guo1" ("finish dyeing"), one can feel that "guo1" only means that the action denoted by the verb has been finished, which is similar to the function of the particle "le".

it is argued that from a formal perspective, "guo1" is a compliment, but semantically it has the function of a particle.


As to the relation between "guo1" and "guo2", it is argued that "guo1" denotes the completeness of the verbal action, and it can be found in the past, present and the future tense, while "guo2" only signifies the completeness of a past event.

"guo1" is the unmarked category, giving no specific information about the time when the verbal action takes place, while "guo2" is the marked category signifying the sense of "happened in the past".


This should lead Thai readers to favor N1 according to the MSH.

the goal of this paper is to test the MSH against the proposals in (2a-e), which predict Thai to be an N2-attachment language.


Although previous corpus counts on this topic have not included context as a factor, some studies have suggested that the matrix clause can favor N1 (e.g., by making the RC informative, Frazier 1990; increasing text coherence, Rohde et al., 2011; allowing for an alternative interpretation, see pseudo RCs in Grillo 2012; also Desmet et al., 2002b, on the matrix clause increasing the N1 preference in a norming questionnaire).

in order to measure the influence of the context surrounding the complex NP, tokens were classified according to whether information inside the complex NP was enough to determine attachment (internally disambiguated; e.g., voice of men that was uttered) or whether it was also necessary to consult the context surrounding the complex NP (externally disambiguated).


different columns in the tables of the relational database), because they provide databaseindependent explanations for these dimensions.

in addition, the involved "mark-execute" mechanism for representing quantifier "all" (as illustrated by the Q, E and X 12 markers in Figure 1) is simplified to a quantification marker "✓" on the student-like edge ( Figure 2), and explained as the division operator q ✓ in relational algebra 2 : the abstract denotations and , correspond to the final results calculated by the original DCS tree according to the wide reading and narrow reading of "all", respectively.


On the contrary, in (8Bii) the expected number is not reached and thus not entailed by the asserted number.

a counter-expectation is generated in (8Bi) but not in (8Bii).


Moreover, she realises that the number of cake that Danai has eaten this time counters her expectation.


According to the common ground information, Danai is not a member of the set under expectation but of the set countering the expectation.

the new information which says that Danai has eaten 10 pieces of cake opposes the expectation.


Now, among the two meanings generated by the structure in (14), the primary meaning is already a CCP of questioning; thus the morpheme L% can only attach to the secondary meaning, i.e., the disjunction p _ ¬p.

the whole A-NOT-A construction with the L% tone expresses a complex speech act, questioning and asserting.


In truth-conditional semantics, both p _ ¬p and q _ ¬q are true in all four worlds.

p _ ¬p and q _ ¬q cannot be distinguished from one another as noted above.


Now, an interrogative operator for TOCH?


Contra Lam (2014a), the semantics of an A-NOT-A question is also multidimensional in that it has a primary question meaning as well as a secondary assertion meaning which indicates lack of 'anticipation of prior expectationrejection shift'.

a-NOT-a questions are anti-bias questions, thus cannot be used in biased contexts, while aa4 questions are simple questions which can be pragmatically rendered into biased questions in biased contexts.


For n < length( ), n refers to the n-th stage in from the current stage 0 .

when n is more recent than m , m > n. To define the semantics of (¬)', Groenendijk (2013) introduces another semantic relation, prior expectation-rejection shift.


Both facts are correctly predicted since HO2(p) semantically indicates that the questioner has an expectation toward p. Similarly, a ME1 question indicates that the questioner has an expectation toward ¬p.

it cannot be used in neutral contexts (3-c) ant can be responded with "You're right" to agree with the negative answer (6).


Semantics of an AA4 question Put another way, it does not encode any expectation within its semantics.


According to the analysis in Section 3, the object of the causative fan does not necessarily experience the mental process of "getting annoyed".

the object should be a We now move to the mental use of fan, which is proven to be exclusive to Beijing Mandarin.


Lakoff and Johnson explain that humans sleep lying down and stand up when they awake.

the concept of DOWN is expanded to being unconscious, and the concept of UP to being conscious.


After testing various thresholds, it was decided to use a frequency threshold equal to 0.001% of the size of each blog.

constructions only appear in the ranked RRF lists if they have frequencies greater than: chimalaya (30), itsgettinghotinhere (8), wattsupwiththat (34).


We envisage a social science researcher using ranked lists of constructions as a starting point to investigate the discourses in one or more (sub-) corpora.

the constructions should provide a convenient overview of the content and draw atten- tion to potentially interesting phenomena, like topics, framing and argument structures.


However, the word used in many contexts, such as boss and start, cannot be said to be that the context resembles the paraphrase target noun even if com(n t , n c ) are large.

we filter it in the latter term of the Equation (1) and lower score of the paraphrase candidate noun used in much context.


The Fleiss's Kappa coefficient of three evaluators is 0.61.

the agreement degree between raters is high enough.


On the other hand, (Bhagat and Ravichandran, 2008) argued that the biasedly cooccurring context is important.

(Marton et al., 2009)'s method depends solely on high frequency words, whereas (Bhagat and Ravichan-  dran, 2008)'s method relies on low frequency words.


However, in this study, we evaluate the performance of our generation method independently from the quality of automatic word or phrase alignment algorithms.

we conduct manual alignment, which is much more accurate than automatic alignment.


Variation models typically include thousands or tens of thousands of observations, in order to ensure enough statistical power.

it is critical to choose an appropriate data source that contains enough relevant observations and adequate representation of the predictor variables.


7will have a problem, i.e., the sum of the transition probabilities from a concept to its hyponyms is not one.

we calculate the probability by considering that the same concept that follow a different path is different: where path(s i , s j ) denotes the number of the paths from concept s i to its hyponym s j and L(s i ) denotes the leaf concepts below s i .


Thus, we calculate the probability by considering that the same concept that follow a different path is different: where path(s i , s j ) denotes the number of the paths from concept s i to its hyponym s j and L(s i ) denotes the leaf concepts below s i .

the transition probability can be calculated by dividing the frequencies of the hyponym by that of its hypernym.


The EDR Japanese corpus did not include the basic forms of words.

we used a morphological analyzer, Mecab 4 , to identify the basic forms of words in the corpus.


LSA_similarity represent a sentence by summing all LSA vectors of words appeared in the sentence and then averaged it with the length of the sentence.

we can get vector representations 1 and 2 of the two sentences.


Secondly, what's more difficult is to recruit highly diverse subjects due to the difficulties in subject recruitment and the spacial limitations of laboratory-based experiments.

researchers heavily and even blindly rely on relatively small sample size which is 30 or so (Sprouse, 2011) and the undergraduate subject pool.


But virtually, for this kind of subjective rating tasks, subjects rarely totally agree with each other and there is always some noise or errors of varied degrees.

the distributions of the results of subjective rating tasks rarely cover the whole scale.


Manual annotation costs a lot, and can only cover some specific category, e.g., person names (Christen, 2006) as well.

we look forward to exploring a paradigm that could automatically generate large scale of open-category training datasets without manual annotation.


However, there are inadequate free texts locally for extracting features, as Freebase is a well-structured knowledge repository with billions of triples.

we resort to other free-text corpus that could be distantly supervised by Freebase and the key challenge is to find the bridge of supervision.


Furthermore, we use Stanford NLP core 7 and add the part-of-speech tagging feature which may help disambiguate those contextual words.

for each K size window surrounding the entity mention, we could extract four kinds of different features, i.e., bag of words (BOW), word sequence (WS), bag of words plus part-of-speech tagging (BOW + POS) and word sequence plus part-of-speech tagging (WS + POS).


However, Table 2 shows that there are at least 5.5 million names that denominate more than one entity (MID) in Freebase.

it is infeasible to build 5.5 million specific classifiers.


For the future work, we believe that this new paradigm leaves several open questions: • Besides the entities (MIDs) that have already been stored in knowledge repositories (Freebase), new entity instances (NIL) with the same name need to be discovered.

further study could focus on extending paradigm to identify unknown entities.


"字" is followed by two unlabeled characters which are then followed by an address, an unlabeled character, another address, and an office.

t2 and t3 are examples of pattern P5, where <name> and <address> represent labeled strings and Z1 and Z2 are two unlabeled characters.


The unlabeled characters, Z1 and Z2, can be extracted as style names because practical statistics indicate that over 98% of style names contain exactly two characters.

we embody this finding with actions in our programs.


Semantic relations between entities are essential for many NLP applications such as question answering, textual inference and information extraction (Ravichandran and Hovy, 2002;Szpektor et al., 2004).

it is important to build a comprehensive knowledge base consisting of instances of semantic relations (e.g., authorOf) such as authorOf ⟨Franz Kafka, The Metamorphosis⟩.


Here, we explore the use of low-dimensional word vectors learned by word2vec from the large corpus: the meaning of a pattern is represented by the distribution of entity vectors.

we obtain the vector representation of a relational pattern p, p = Here, v e 0 denotes the vector for an entity e 0 in the entity pair e, and v e 1 does the vector for another entity e 1 in the pair e. For our experimental corpus, we collected 15 billion Japanese sentences by crawling web pages.


To remove noise such as spam and non-Japanese sentences, we apply a filter that checks the length of a sentence, determines whether the sentence contains a specific character in Japanese (hiragana), and then checks the number of symbols.

of filtering, we obtained 6.3 billion sentences.


Concretely, for obtaining pattern feature vectors using word2vec, we concatenate the sum of vectors assigned to one side of entity pairs and the ones assigned to the other side.

there is a possibility that we obtain pattern pairs with a high similarity when both of the patterns contain one of the same entity types.


On the other hand, the computation time of APPROX-PMI+PCA was much smaller than that of EXACT-PMI and APPROX-PMI.

it is necessary to reduce the amount of dimensions because APPROX-PMI would take 7,441 hours (about a year) to calculate all pattern similarity with one thread.


If w appears in i-th bi-sentence of the parallel corpus then V wi = 1.

all input neurons corresponding to current word w are set to 0 except those that correspond to bi-sentences containing w, which are set to 1.


It is also important to note that a single RNN tagger applies to German, Greek and Spanish; so this is a truly multilingual POS tagger!

as for several other NLP tasks such as language modelling or machine translation (where standard and NN-based models are combined in a log-linear model), the use of both standard and RNN-based approaches seems necessary to obtain optimal performances.


For explicit PPs, since the left and right boundary words are collocation, they can be labelled with special marks in the knowledge base and can be first identified.

after identifying them as the boundary words of PP, the whole PP chunk will be recognized easily.


For example, the word vectors "dog" and "cat" should share "animal" or "pet" meanings to a certain degree, but 1-of-K representation is not able to capture this similarity.

we propose distributed word representation.


According to Mikolov's work, skip-gram shows higher accuracy than CBOW 1 .


We define n i to be the number of hidden nodes of the ith layer.

using Equation 4 the dimension of weight matrix W 1 will be n 1 × d. Similarly, the weight matrices up to the l − 1th layer will be W i ∈ R n i ×n i−1 (i > 2).


That indicates the relationship between the number of layers and connections is linear, but the number of connections grows exponentially with the number of nodes.

a small increase in the number of nodes results in a long training time.


In (Atdag and Labatut, 2013), Atdag and Labatut compared different named entity recognition tools' performance, and the Stanford NER 9 showed the best results.

we apply Stanford NER to extract the concepts.


In (Bellman 1961), the author referred it to as "the curse of dimensionality".

dimensionality reduction is a major research area.


In the other hand, ( , ) presents such a good intercategory condition but does not mention any conditions of term for intra-category .

according to the conclusion of CMFS (Yang et al.


Concretely, if the separation degree of category is greater than that of category from the other categories, presented terms of category obviously have higher score compared with those of category .

after the term score ranking, there are a large number of terms supporting category to be selected into the final set, while it does not contain enough terms for classifying category .


As analyzed in this paper, this problem is directly caused by the imbalance of the number of training documents between categories and the imbalance of the separation degree between categories.

in ExFCFS, we adjust FCFS score of a term with respect to a category in inverse proportion to these factors in order to improve the classification performance of rare categories and poor separation categories after filter feature selection process.


10, FS methods having better performance at smaller number of selected features are preferred.

dimension reduction rate of FCFS is better than that of CHI, IG, CMFS, and OCFS as presented in Fig.


"LOVE") to emphasize certain sentiments or emoticons.


they are incoherent), conflict of the polarity may not indicate the sarcasm or irony.

the module 2 identifies coherence in a tweet and uses it as a feature.


After conducting a preliminary experiment, we found that the EM (expectation maximization) algorithm outperforms other approaches, including hierarchical, k-mean and DBScan, in the identification of coherence in tweets.

eM algorithm is used to cluster the tweets into two groups, one for coherent and one for non-coherent tweets.


Accordingly, the average polarity value of words computed by our baseline system also indicates the moderate negative range.

baseline 1 achieved a high accuracy and also became competitive with other methods.


As can be seen, the result shows that all features have taken part in the method to enhance the accuracy, except for BiToken.

we can easily conclude that BiToken is not a relevant feature for sentiment prediction of figurative tweets.


Although this assumption could be too strong in general, for our sole purpose of investigation, our focus here is on text form of the news.

we proposed to construct a set of these 'texts' to be used as features in order to classify Thai stock news into three sentiments: positive, negative and neutral classes, using known sentiment news as a training set and unseen news as a testing set.


The training set contains no neutral sentiment.

there will be only two classes of classification results instead of three.


With the expanding growth of social networks services, user generated content web has emerged from being a simple web space for people to express their opinions and to share their knowledge, to a high value information source for business companies to discover consumer feedbacks about their products or even to decide future marketing actions.

opinion mining is becoming a potential research domain interesting more and more researchers who attempt to improve current results and to solve more advanced and complex issues in the domain.


In fact, we remark that each researcher has collected his own datasets and used in the evaluation of his classification approach, which does not allow comparing properly the obtained results.

we have decided to use in our experiments existent datasets that have been widely used by the NLP research community.


It focuses on either annotating corpus with discourse information (Al-saif and Market, 2010) or proposing taxonomies of discourse relations (Khalifa et al., 2012).

it is not possible to profit, in Arabic language, from an automatic generated discourse structure or an automatic recognition of discourse relations to improve sentiment classification.


However, these features are domain dependent; they cause a big decrease in the performance of the classifier when testing it with other data collections.

we have decided to minimize the effect of domain dependent features by excluding unigrams and relying only on bigrams and trigrams.


Ziji in (4) is co-referential with the first person pronoun wo 'I' rather than with the source Lisi.

the long-distance bound ziji cannot always refer to a source of communication, as in Sells' (1987) system, and thus in this case logophoric ziji does not seem to be a sufficient condition to independently license its antecedent, unlike logophoric pronouns.


(Kuno and Kaburaki 1977: 645) The two sentences in (19) and (20) fundamentally deliver identical situations in their logical content, but they seem to differ from each other in the standpoint from which the speaker has intentionally chosen to describe the events, and empathize more with a specific person.

it can be easily presupposed that the speaker empathizes more with John than with Mary in (19), while the speaker empathizes more with Mary than with John in (20).


If the empathy locus is anchored to the addressee, then ziji can refer to the second person pronoun ni 'you' referring to the addressee, as in (5d).

this empathy relation is compatible with the Speech Act Empathy Hierarchy.


(13) *Bill gave the girl who [ate it, the potato salad], a dollar.

biclausal + deletion analyses such as that of Whitman (2000) have empirical problems.


Here, however, the final attachment site in the jar (=β) does not c-command the original attachment site into my mouth (=α); this results in a high-cost reanalysis.


Then, John is nondistinct from the trace of he in terms of semantic features and semantic type.

(26) allows John to be construed as an element sharing properties with the trace (i.e., he).


His and the dislocated NP are non-distinct in terms of semantic features and semantic type.

the dislocated NP and his can be construed as sharing properties.


According to the URC in (19), however, this reattachment is impossible: the final attachment site fails to c-command the original attachment site of the potato salad.


Similar to pseudo-passives, the logical object of "object-fronting" is in the subject position.

many often confuse "object-fronting" with "pseudo-passive".


In Mandarin, "V-de" marks either a degree complement or a state complement whereas "V-de" in Cantonese can only be used in marking degree complement and "V-dou3" is used to mark state complement.

it can be concluded that V-de in Mandarin corresponds to V-dou3 in Cantonese.


RVC and V-dou3/ V-de are always interchangeable in Mandarin, while they are sometimes restricted in Cantonese.

v-dou3 in Cantonese should not be deemed as a dummy like v-de in Mandarin.


According to Agresti (2007), a generalized regression test is available when the distribution does not follow the normal distribution.

the test was adopted to examine how each factor affects the acceptability of the sentences.


the exact properties for this tendency have to be investigated through the further research.


Some were positively skewed, and others had a slightly bimodal distribution.

a non-parametric testa generalized linear regression testwas performed with a Gaussian distribution, in order to examine how the Possessor/Adjunct distinctions affected the acceptability of sentences.


Many of our A-type sentences had a salient MSs and characterizing SPs as in (9c).

this could have contributed to participants' higher ratings of naturalness of the MSC sentences.


If no rule can be matched, only the glue rule can be applied.

the prepositional subtree "为 了...义务" cannot be correctly reordered to the end in the target side.


However, our final goal is to combine lexical items with prosodic and acoustic features.


We propose that the similarities can be explained by the cognitive characteristics of the sound, while the differences are determined by the influence of the semantic dichotomy of production/perception on synaesthesia.

linguistic synaesthesia is not a random association, but can be motivated and predicted by cognition.


Specifically speaking, 聲 is concentrated on the auditory production, while 音 is focused on the auditory perception, although both can refer to the sound (Hong and Huang, 2004;Hong and Huang, 2005).

through studying the synaesthetic usages of this pair of near synonyms, not only can we test whether synaesthesia is sensitive to the semantic dichotomy of production and perception, but also we can examine the relationship between synaesthesia and cognition.


General speaking, as 聲 and 音 both belong to the auditory sensation, other modalities can transfer frequently to describe these two near synonyms, of which morphemes from both visual and tactile domains can be used to modify 聲 and 音.

hearing is also a productive target domain of synaesthetic transfers in Mandarin Chinese.


Both 聲 and 音 are in the auditory domain, which can denote the sound, thus, both of them can inherit common semantic information from the upper concept, namely sound.

we think that the similarities on synaesthesia between 聲 and 音 are determined by the whole synaesthetic characteristics for hearing, that is, hearing is a productive recipient of synaesthetic transfers and is much easier to be the target domain of vision than touch.


Compared with the ending point of the sound, humans can impose much more active actions on the starting point, whereas the perception and evaluation can be received much more passively.

the production of the sound is conceptualized and characterized more frequently than the perception through synaesthesia.


In other words, synaesthesia can be predicted and determined by the cognitive modelling.

besides the transfer tendency recognized by previous studies, linguistic synaesthesia exhibit another regularity, that is, it can be determined by cognitive motivations.


Therefore, besides the transfer tendency recognized by previous studies, linguistic synaesthesia exhibit another regularity, that is, it can be determined by cognitive motivations.

linguistic synaesthesia is not a kind of random associations of different sensations, but of many principles and regularities, which deserves much more attention and deeper studies.


However, this would be a very formidable task both to create them and to maintain them while keeping them up to date.

a datadriven approach that makes use of the huge amount of conversational resources currently on the web, such as microblogs or social network media, as corpora have been recently investigated (Shibata et al., 2009;Sugiyama et al., 2013).


They also support highly interactive customizable browser interfaces and many APIs for connecting to other services provided outside themselves.

we can define our own markers that can be used for annotation, or we can send utterances to bot servers outside the system for watching the progress of a conversation or violations of the guidelines in realtime.


The chat logs can also be exported using those APIs.

we found these are ideal environments for collecting chat data, annotating them online, and remotely managing them.


7 and 12 do not qualitatively contradict each other as well.

we can confirm all the workers earnestly conducted their evaluations.


The production of such a gesture then helps the person utter the word for that concept in language.

gestures are thought to be dominated by the computational stage in which a lexical item is selected from a semantically organized lexicon (Butterworth & Hadar, 1989).


Understanding abstract concepts in terms of objects then allow us to project various experiences of object to the concepts.

it is likely that entity metaphor is frequently used to conceive abstract concepts.


However, this may lead to a discrepancy between listening/speaking and reading/writing skills at a later stage.

the choice of curriculum depends on when it may be best to introduce reading and writing skills to adult CFL beginners.


In addition, Chinese consists of a large number of homophones, which allows a syllable to correspond to many different characters with various meanings.

phonological information is unlikely to be as reliable as the orthographic form of a character in reading comprehension (Cao et al.


At the character level, a single character usually represents a single morpheme and a character usually needs to combine with another one to form a word.

it is essential for Chinese learners to be able to recognise a character and activate the morphological knowledge from the visual input in order to go from comprehension of a word, to a phrase, and then to sentences and texts.


This coupling may help establish the spatial configuration of strokes and radicals, which along with a temporal sequence of motor movements associated with stroke composition, completely defines the shape of the character (Cao et al., 2013b;Guan et al., 2011).

significant spatial analysis is intrinsic and highly organised motor activity is involved in writing a Chinese character (Tan et al., 2005).


Although the study may be interpreted as showing support for curricula that prioritise writing over other language skills, the results actually show that learning both the character and its pinyin simultaneously does not negatively affect character recognition.

some sort of combination of sound and character training, as exemplified in the training paradigm used here (see Figure 5), may turn out to be best.


However, presently, learner corpora are only helpful in terms of evaluating the accuracy of learner output (speaking and writing).

the present study proposes a learner corpus annotated with evaluation results regarding the accuracy and fluency of performance in speaking (output) and listening (input).


the present study proposes to annotate listening comprehension data for individual sentences, which is expected to offer a finergrained analysis for the identification of learners' linguistic problem areas.


Since the target of this project includes beginner-level learners, the listening task should be fairly easy to complete.

listening material is obtained from the VOA (Voice of America) Learning English site (http://learningenglish.voa news.com).


Hence, it is necessary to first assess the difficulty of authentic materials, and then to provide materials that are appropriate for the individual learner's proficiency, which is a rather burdensome task for language teachers.

automatic assessment of the materials' difficulty supports both effective learning and effective teaching.


Even further back, in Plato's time, there was a general agreement that one can only speak of what one is consciously aware of.

it is reasonable to view infant language acquisition process from the perspective of consciousness.


However, a baby-agent with a higher LoC can realize that some agents or signals are special for it.

it will become conscious of other agents' identities as well as its personal connections with them, and thus begins to build up new protocols or mechanisms that channel its interactions with other agents.


Learning abilities can also be expressed as rules, functioning in a similar way as Hebb's (1949) learning rule in neuro-psychology.

these meta-rules for learning are clearly distinguished from the signal-processing rules.


However, consciousness development is a continuous process, and the increase in LoC is a quantum leap from the accumulation of drip growth.

before determining at which level of consciousness a baby-agent is situated, we must know the factors that influence the development of a baby's consciousness capability.


However, the enrichment of the proposed structure and even that of the lexicons proposed thereafter was such a hard task that it could not be accomplished due to the varied and abundant knowledge to be represented and requiring a high linguistic expertise.

this enrichment task is an expensive and timeconsuming process.


It represents an example of use by a simple sentence.

a meaning of a lexical entry in the LMF dictionary can be attached to different SBs and it is described by Contexts of text strings.


Also, the search for the second token of the (C) "apple" in the dictionary finds two SCs: "plants:tree" and "plants:aliment:fruit".

the base of rules identifies the "aliment:fruit" SC for the (C).


The purpose of this step is the construction of the SP class identified by the recognition of SCs of semantic arguments.

the combination of those classes composes the SP.


In 16, the embedded izyooniclause itself is a comparative sentence.

the degree argument position of nagai 'long' is bound by DegP within the izyooni-clause.


The same thing happens in the matrix clause.

izyooni-comparatives are a comparison of two differential degrees.


the degree in the embedded clause needs to be larger than a given standard in the real world.


However, the two clauses involved are non-parallel.

no full clause-to-small clause correspondence is called for.


As it is calculated for each individual tested by using the acceptability-rating experiment, it serves as a measure of the superadditive component of the interaction for each individual and for each island type.

the score is thought of as the strength of island effects for that individual.


29Dalu cengjing fasheng guo mainland before happen PERF wenhua da geming, ta suoyou de culture big revolution it all DE dongzuo he xingwei, he rujia shi action and behavior with Confucian be nayang de bu xianghe…(SC) so DE NEG consistent 'The cultural revolution once happened in Mainland China.

the actions and behaviors there are not consistent with Confucian (culture)…' Despite the semantic similarity, dongzuo and xingwei, which are nominal markers in this study, crucially differ in terms of their interaction with classifiers.


However, they proposed constraints focusing on zero subjects mainly.

it is hard to apply their approach on zero object resolution.


The topic of the sentence can be detected easily in Korean using the topic markers "은"(eun), "는" (nun) and delimiters such as "도"(to), "만"(man).

it is likely a candidate antecedent is the antecedent of the zero object if the candidate has one of the topic markers or delimiters.


This is one of examples showing the property-sharing constraint.

the parallelism of syntactic function between a zero object and a candidate antecedent can be utilized.


In the previous literature, it is argued that a salient entity recoverable by inference from the context is frequently omitted (Walker et al., 1994;Iida, 1998;Hong, 2000).

we utilize the forward looking center ranking for Korean, assuming the most salient candidate antecedent which is marked as a value 1 is likely to be the antecedent of the zero object.


It may not be quite fair comparison if we compare our result with the results of other studies on Korean written texts.

we set a baseline by choosing the most salient candidate in the discourse according to the forward looking center ranking in Hong (2011) for comparison.


As mentioned previously, since HWS structures are essentially binary trees, directional information has already been encoded when HWS structures are established.

after an HWS structure being constructed, directional information can be easily attached to this tree as shown in Figure 2.


Perplexity is not a definite way of determining the usefulness of a language model since a language model with low perplexity may not work equally well in a real world application.

we also performed extrinsic experiments to evaluate our model.


With this information, HWS models can be build more syntactically appropriate while remaining its original advances.

higher performance can be achieved, both intrinsic and extrinsic experiments confirmed our thoughts.


Instead of replacing BNLMs with NNLMs in IME, we propose to use NNLMs to enhance BNLMs, which means recalculating the probabilities of ngrams in the BNLMs with NNLMs.

we take advantage of the probabilities provided by NNLMs without increasing its on-site computational cost.


However, it suffers from data sparseness, that is, the probability of the history h i not appearing in the training will be zero.

we need smoothing technologies to alleviate this shortcoming.


In the case of a particular NNLM, it will provide the same probability distribution given the same input history.

we can use NNLMs to calculate the probabilities of all the possible n-grams in advance and save the results, which is just like constructing the BNLMs from CSLMs.


For decoding a sentence in IME, generally, thousands of language model accessing is usually required.

according to the above empirical results, integrating NNLM into IME without any modification will result in obvious response retardation, which gives a poor user experience.


The selection is based on the following rules: (i) microblogs posted by experts should focus on the type we are interested in (i.e., news), (ii) experts should be active, so that they provide time-relevant microblogs to be used in training our classification model.

a candidate expert is more professional if a large percentage of his/her microblogs belong to the type.


Moreover, the ensemble prediction score (output of Equation 1) is proportional to the confidence of the predicted label, which may be independent to the importance of the classified news post.

we propose a novel ranking method for newborn news microblogs, called Behavior-Actor-Venue based Sorting (BAV sorting, for short).


However, knowing that m 1 is very important does not allow us to conclude that m 2 is, since the importance value of m 1 might also stem from Beijing.

we can infer that, considering a microblog m i of M tr and a microblog m j of M te , if the term set of m j contains a large proportion of terms in m i , then V I (m i ) is transferred to V I (m j ) with high confidence.


We introduce three characteristics and a scoring method to the bootstrapping.

the iteration process on the bootstrapping increased the number of tweets and patterns for trouble information dramatically.


In other words, the ratio of positive and negative instances for this task tends to be biased.

machine learning approaches are not always suitable for this task.


Although we utilized some features about emoticons, Internet slang dictionaries and so on, they were not effective.

we used only the bag-of-words features for SVM.


In other words, this situation is not on the real world.

we also evaluated our method trained by 900 tweets with 30,000 tweets that extracted from Twitter randomly, as an opened test set.


Although trouble sentences (TSs) always contain TE(s), all sentences with TEs are not always TSs.

we also prepared NG phrases for the rule-based method.


hand, the number of tweets extracted correctly was reduced almost by half (720 vs. 444).

the simple rule-based method faced with another problem for this task.


The method in this paper does not handle any patterns for the bootstrapping process.

we cannot incorporate this algorithm into our method directly.


As a result, our method could not obtain TSs and new TEs in the next process.

our method needs an adequate amount of tweets for the TE acquisition process.


This model demonstrated that we were able to predict consumer behavior with tweets and our value system.

we applied it to marketing consulting and social contribution for trial-and-error.


Moreover, we think that personal values are interactive to each other.

we constructed our TwitterSocietas Model with a Bayesian Network approach.


We evaluated the performance of the model and compared it with SVM and NB.

we found that our approach accurately inferred personal values with skewed training data.


As shown earlier in Table 1, we assume that the language used in tweets can express difference in pitch or tone of voice as a written text in tweets.

we aim to capture such difference in voice-reflected tweets using language models.


Moreover, we assume that the minimum duration of an event is a minute.

we aggregate stream of tweets from the same minute into one set of tweets and compute the perplexity of it.


An annotator looks at all of the tweets from the same minute and annotate whether each timestamp is an emotional upsurge or not.

42 timestamps in #aibou, 44 timestamps in #hanshin, 9 timestamps in #ACV, 37 timestamps in #figureskate, 86 timestamps in #agqr and 63 timestamps in #momoclo are annotated as emotionally upsurging timestamps.


We use this reconstructed training data to perform document classification by NBEM.

of experiment by using a part of 20 Newsgroups, the effect of this method was confirmed.


As for the experiment we used the 20 Newsgroups data 1 to construct the domain A and the domain B, and then domain adaption experiments were conducted from domain A to domain B and from domain B to domain A.


The weight of the source domain is reduced according to EM iterations.

both of NBEM and ANB gives weight to a feature through the class distribution of target domain.


The P s is the distribution of the following set: The P t can be defined by the same way.

in this paper, first, define the distance d(f ) between P s and P t as following: Then set the weight by using d(f ).


We use Naive Bayes as a learning algorithm, so the value of feature becomes frequency.


NBEM+STFW in the table is the our proposed method.

of SCL and uLSIF has not changed a lot that both of them is based of SVM, there is a high overwhelmingly accuracy toward NBEM+STFW.


NBEM is used to learn a classifier, and then the learned classifier and SFTW reconstruct training data, and then the final classifier is learned by using the reconstruct training data and NBEM again.

of experiment by using a part of 20 Newsgroups, the effect of our method was confirmed.


The highest accuracy (73.0%) was achieved for α = 0.2.

α was set to 0.2 for the subsequent experiments.


Machine learning with unbalanced data usually leads to generation of a wrong classifier.

we adjusted the number of sentences in the training data (use s ) for each classifier by using the following equation.


Nonetheless, it is difficult to provide test and training data concerning distinct large scale disasters.

we tried another setting in which the tweets posted during the two days immediately after the Great East Japan Earthquake were used as training data and the tweets posted after the first two days were used as test data.


Since that the purpose of negation recognition is to detect false rumors during a disaster, taking one month to annotate the data and to train classifiers is too long.

we simulated the situation as in Figure 2.


There were also many tweets to negate such false rumors.

we used the content of the false rumors for training to recognize other negated predicates whose content is the same as the trained ones.


Each microblog has several topics labeled by sentiment words following different multinomial distributions.

a suicidal-sentiment layer can be extracted from annotating each sentiment word and computing the sentiment polarity that is associated with words and topics in documents.


We also observed that the number of critics, like, retweet surged after the suicide was reported publicly.

we took three meta features into consideration: posting type, posting time and social relationship.


(2009) reported that SVM outperformed other classifiers where ngram and POS features are used and unigram feature worked the best for traditional and twitter sentiment analysis respectively.


Sentence Sentiment Score The first sentence and the last sentence are always more important than others.

we compute sentiment scores of them respectively.


This indicates that message sentiment is mainly decided by tweet text and its surrounding information.


There are only 8 positive samples in Training and only 1 of them are classified correctly.

the prediction for positive tweets is unreliable.


The former is the truth-based system (e.g., Korean) whereas the latter is the polarity-based system (e.g., English).

what matters in polarity answers is the 'anaphoric potential' of the polarity particle and the 'polarity sensitivity' of the question-underdiscussion (QUD).


Acknowledging that language plays a vital role in the formation of a national identity, and hence, should be cultivated towards intellectualization, I propose that ICT could effectively be used to monitor language usage and provide added insights for language planners, in understanding the interplay between language and socio-political developments.

for this talk, I will also describe various ways by which corpora could automatically be harnessed and analyzed, and used to identify areas of language use that could be highlighted or further improved.


For example, the choice of verbs aru / iru (i.e., "exist") is based not so much on a distinction between animate and inanimate but between sentient and insentient.

we needed to set the parameter of sentience.


SFPs in the Japanese clause Japanese is a strictly left-branching language, hence elements further right in linear order generally scope syntactically higher and enter the semantic derivation later than such further left.

layered clause models have been proposed in descriptive Japanese grammar.


Focusing on evidence-related felicity condition on assertion, I define evidence propositions of the form EV w x ϕ relative to belief propositions in the following defeasible entailment relation.

10 (7) a. x ϕ from the premise that x has evidence supporting ϕ at w one can infer by (7-a) that x believes ϕ at w as in (7-b), unless there is an additional premise that x believes ϕ to be false at w as shown in (7-c).


FIs, on the contrary, not only presuppose ¬B c S ϕ, but also give rise to the forgone commitment implicature ¬P B c S ϕ.

when ne occurs in FIs, it indicates a persistent discrepancy between speaker and addressee belief rather than consensus.


According to Köhler & Naumann (2010) and Köhler (2015), linguistic motif is defined as: The longest continuous sequence of equal or increasing values representing a quantitative property of a linguistic unit.

a L-motif is a continuous series of equal or increasing length values.


However, the above discussion shows that the sentence with no koto is a control construction and that there are two types of omow 'think'.

we propose the following lexical entries for two types of omow'think': It should be noted that in a raising verb (8a), an accusative-marked NP is located outside XP against the Exceptional Case-marking (ECM) analysis (Kaneko, 1988;Ueda, 1988;Hiraiwa, 2001;Taguchi, 2009) in which such an NP is located inside XP as shown in 9a In (9b), when Naomi is marked with ga, the complement clause XP containing it can be clefted.


This test suggests that an accusative-marked NP is not a constituent of XP.

we regard RTO construction without no koto as having the feature structure as (8a), and propose two types of lexical entry of omow 'think' as shown in (8).


On the other hand, the embedded subject is specified as NP[nom] because the nominative case is specified by some lexical property of the stage-level predicate.

structure-sharing between them with 1 is not possible, consequently RTO is not licensed.


Our method could simultaneously segment bilingual corpus and generates corresponding bilingual lexicon.

our work also can be treated as a bilingual lexicon induction.


The current alignment tools will filter a large amount of characters alignment results.

the bottom-to-top method cannot be applied.


derivations:ẽ = arg max e P (e, D|f ) (13) where each Φ i is a sub-model score function and λ is the corresponding weight.

for each arbitrary score function Φ i with a derivation D, we decompose it as a chain of independent derivations d with BTG rules X → γ: given an input sentence f = f 1 , .


It has no NE annotations and originally designed to support statistical machine translation in Arabic NLP.

in this research, these datasets have been manually annotated in order to support the NER task.


We have halved the number of gazetteers.

we simplified the extraction process and we noted a considerable gain in the response time.


The mean age of the individuals who participated in the study was between 16-19 years old.

a depressed Tumblr user is characterized by photo content in posts rather than just pure textual posts, posts are more negative, and the evident use of self-referencing words.


Under the same conditions, a better language model brings a better translation system.

we also evaluate our language model by evaluating the translation system who uses it.


This can be the result of the subjects not wanting to take the responsibility for themselves and also wanting to defend their lies.

the subjects tend to use the word 'we' with the intention to build a perception as if many people support what they say.


It can also be caused by not having any evidence from the outside world to support their ideas.

they choose to say it with 'I think' instead of answering the interviewer's questions directly.


Labov [11] denotes that the instability, usually age or class differences within a phoneme is a signpost for socio-phonetic change.

the present study brings about a new possible explanation of foreign influence to the sound change of Cantonese in the current multilingual landscape of Hong Kong.


Such an explanation differs from mainstream theory suggesting these changes being mostly intrinsic within the evolution of a single language.

as backed up by previous literature, the study intends to investigate the following research questions: 1.


For the control group, 100% sound were pronounced as /t h / as predicted, and we did not see any of the tokens with a palatalized sound change.

no further statistical comparison was done for the control group.


In other words, it was not possible to create single large vocabulary that would eliminate the out-ofvocabulary problem.

it was not possible to create a language model that would cover all the words of any language.


(Park 2016:102) We assume that the first person pronoun nay in (5) functions as a blocker since it is unnatural for caki to refer to the matrix subject Hyengsa in this discourse.

2 based on the observed fact, this pa-2 Some may claim that (5) is a kind of a special occasion in this context and thus the blocking of caki's referring to hyengsa is attributed just to the lexical property of phaylyunpem 'reprobate', which means to harm one's own lineal ascendant and descendant.


However, for simplicity and clarity, the scope of the investigation of this paper is limited to conditional and causal complex phrases, since -(ul) kes-i is often realized in single phrases as a continuity of causal or conditional statements, as in "Drinking two grams of cyanide causes death", which is approximately the same as saying "If somebody drinks two grams of cyanide, they will die" (Puente, et al.

2009) God-Acc resemble-Adn thing-Cop-Dec 'the image of the future men will resemble God Atlas.'


Moreover, Bruening (2016), while admitting that -ed adjectives as in (1) and (2) are formed from nouns, suggests an analysis whereby their derivation involves an intermediate, non-existent verb form derived from an N and meaning 'possessing N', with the verbalizer being a null affix corresponding to the English prefix be-, as in bejeweled, beringed, beribboned, etc.

this analysis treats the -ed adjectives in question as deverbal rather than denominal.


In fact, a cursory search of the Oxford English Dictionary found no examples of be-prefixed -ed adjectives based on modified nominal bases, suggesting that be-could not form verbs with modified nominals even when it was most productive in its history.

i conclude that, even though the phonologically null version of the verbalizing prefix be-might attach to a bare nominal base and feed the formation of -ed adjectives as adjectival passives, this analysis cannot be extended into covering cases involving modified nominal bases.


10 In these nouns, the suffix in the singular form is an exponent of n, and the suffix in the plural form is more specified and is an exponent of n and the plural feature fused together.

in the case of singular curricul-um, whose structure is represented in 11 In this analysis, what appears to be the singular and plural suffixes in the above foreign nouns are the exponents of n and the feature complex of n and [-singular], respectively.


Specifically, the adjectivizing suffix and the possessive D are crucially different in that the latter, which is semantically transparent as in (25)a below, can undergo type-shifting so that it can accommodate non-relational possessees, while the former cannot.

when a possessee is a non-relational noun, the type-shifting operator in (25)b kicks in, thereby allowing the possessee to be in a free, pragmatically controlled relation with the possessor (Barker, 1995; As a result, both relational and non-relational nouns can appear as a possessee in possessive DPs, as shown in (26) and (27), respectively.


This meaning shift basically explains why denominal -ed adjectives based on this class of nouns are used for situations where they are worn, not possessed.

it seems plausible to say that, for type-shifting of non-relational nouns to relational ones to be successful, concomitant meaning-shift must be such that it supports the relational interpretation.


According to Murar, Trantescu and Pisoschi (2011), "time adverbials can be a hierarchical relationship: the order of the adverbials depends in part on information focus, but the tendency is for the adverbial denoting the most extended period to come last" (p. 55).

one morning precedes last April showing this hierarchy.


We find that 6.5% (304 sentences) of the sentences which contain bale and 5.2% (452 sentences) of the sentences which contain eryi cannot be used interchangeably.

our dataset has 756 sentences which the two SFPs cannot be used interchangeably.


Benveniste (1971) argues that language is the instrument of communication and taken over by the man who is speaking and within the condition of intersubjectivity.

people can use a lot of methods to express their mood and tone.


the proximity of equivalence in the process of mapping between two words from two languages being acquired plays a crucial role.


Interestingly, owing to the bi-syllabic structure lexicon formation of Modern Chinese, both the monosyllabic lexicon xīu and jiǎn could combine into a bi-syllabic lexicon xīu jiǎn to denote specifically the cutting of a small part off something especially to make it neat using scissors.

the above-mentioned 3 lexical features in the denotational dimension distinguish the subtle nuances of the semantic meaning of each of the synonymous verbs respectively, though with some occasional overlap among these features.


Nowadays, Arabic paraphrase identification based on semantic similarity analysis between source text and suspicious text is a difficult task in Natural Language Processing NLP of which we examine the similarity degree of a given pair of texts, in varying in different levels such as word, sentence or paragraph (Vo et al., 2015).

many distributional semantic approaches based on the resemblance determination of their signification and their semantic contain (Negre, 2013) have drawn a considerable amount of attention by research community.


Arabic language is very rich of morphological and typographical features (Meddeb et al, 2016) (Zouaghi et al, 2008 which make Arabic semantic analysis a very difficult task for several reasons among which we can mention: • Arabic language is very rich of morphological features.

arabic script is cursive whose most letters are tied and written from right to left whose letters change shape depending on whether they appear at the beginning, middle or end of the word, on the one hand, and it consists of: a stem composed by a consonant root and a pattern morpheme; more affixes include time markers, sex and/or number; and enclitics include some propositions, conjunctions, determinants and pronouns.


This section provides an overview on related works that deal with Arabic plagiarism detection especially in paraphrase identification field based on semantic analysis to determine the relatedness between the suspect and source Arabic text documents.

several similarity detection approaches between documents have been proposed in the literature of which there are three types of methods for computing relatedness according to the type of resources that have been used, we distinguish: • Knowledge-based methods relies on some form of ontology using WordNet which is a well-known knowledge source to compute semantic similarity between words as in (Shenoy et al, 2012) that proposed a semantic plagiarism detection system using ontology mapping where ontologies are a computational model of some domain of the world by describing semantics of terms used in the domain.


• Web-based approach gathers cooccurrence statistics based on the search engine results and used that to compute word relatedness like Point-wise Mutual Information PMI (Niraula et al, 2015).

(Shuka et al, 2016) showed that the use of a web based cross language semantic plagiarism detection approach helps authors and written to secure their files and to make their files sale.


Also, (Hafeez and Patil, 2017) showed that the author analyzed summary of Chinese expression habits using an adaptive weight of word position algorithm based on TF-IDF to dynamically determining the weight of a word position according to the word position.

it introduced the Vector Space Model VSM and designed comparative experiment under the scene of Chinese document clustering of which TF-IDF-AP algorithm improved a promising results.


In this context, we propose a method for Arabic paraphrase detection based on the identification of similarity between source document and suspect document using Natural Language Processing NLP techniques.

our proposed approach allows extracting their semantic similarity to detect paraphrase which can be created by direct copy of sentences, replacement of words with similar ones, and changing the order of words or reconstructing the sentences (Sindhu and Idicula, 2015).


• Term Frequency-Inverse Document Frequency TF-IDF: is calculated for each term in the document by combining TF and IDF as shown in the following equation 3: Word Embeddings "word2vec": Word embeddings are based on a probabilistic feed forward neural network language model to learn a space of continuous word representations in which similar words are expected to be close to each other.

word embeddings allows representing words with low dimensional and dense real-value vectors which capture useful semantic and syntactic features of words (Law and al, 2017), on the one hand, and reducing computational complexity, on the other hand.


However, because of the number of pairedsegments in Bible corpus is approximately 5 times than the other corpus, it contributes much more than the other corpus.

when translating common phrases, it produces uncommon translation which will make the translation difficult to understand.


However, in the vocabulary list of JLPT, numerous Japanese-Chinese homographs are classified as difficult levels (N2 and above) without consideration of learners' differing mother tongue background.

we attempt to construct a list of Japanese-Chinese homographs that is likely to be helpful in estimating complexity of example sentences that include Japanese functional expressions.


Having an educated workforce is also seen as a desirable social and political goal.

issues related to education are discussed in Hong Kong policy addresses and in speeches by Chinese premiers, which allows us to examine metaphorical uses of the concept of 'education' with an eye to further understanding how education is viewed in these societies.


(2) Mwues-ul ne-nun [John-i ___ sa-ss-nunci] What-Acc you-Top J-Nom buy-Past-Q a-ni?

know-Q the wh-phrase can be placed in the gap position without degrading grammaticality.


There also exist some studies which treat the wh-scrambling with reference to prosodic structures, suggesting that ambiguous scope reading is possible in specific prosody patterns (Ishihara, 2002;Kitagawa and Hirose, 2013 among others).

in order to complement the former studies, this paper examines whether Korean native speakers have different intuition towards wh-PF-island effect and wh-LF-island effect.


Also, answer type was designed following Kim and Goodall (2016).

the current experiment was conducted under a 2 x 2 x 2 factorial design, with three factors: Structure (non-island/island), Whposition (non-scrambling/scrambling) and Answer type (yes/no-answer/wh-answer).


Following Saito (1989), the scope for wh-words is the entire sentence in nonisland sentences.

wh-words can freely take scope in matrix clause.


Wikipedia is the central infrastructure for knowledge curation, as exemplified by Freebase (Bollacker et al., 2008) and Wikification (Mihalcea and Csomai, 2007).

we base Wikipedia articles for building a corpus with causal relation instances.


A side product of this approach is the varying degree of confidence for each span in the corpus.

this corpus provides useful hints for further improving the annotation process for causal relations.


If such annotations are judged by exact match, almost all of them will be assessed as incorrect.

we incorporate the verification process of the test questions in brat, and feedback the annotation quality of a worker to the crowdsourcing service.


In other words, spans should be aggregated when two or more annotations are exactly matched.

the data created by 2-match aggregation were used in subsequent experiments.


We found that 53.0% of verb phrases were re-annotatable as noun phrases.

it may be sufficient to limit annotation spans to noun phrases in the guideline.


Unfortunately, the length of the spans to be annotated cannot be clarified.

determining the noun phrases prior to an annotation work may be unreasonable, but allowing crowd workers to choose their segment boundaries might be necessary.


Since a VO compound as a word is thus a lexical unit whose internal structure is of a V+O (Her, 1997), and an important feature that distinguishes a lexical units from a phrase is the lexical integrity.

based on the previous discussions, it has become a common belief among linguistic researchers that there is a strong correlation between the transitivity of VO and whether the VO is separable (the lexical status), i.e.


Although the numbers and percentages can reveal the difference, they cannot tell whether there is significance or not.

it is important for us to investigate this issue in a more empirical and quantitative way, with the assistance of large-scale corpus as well as the statistical tool.


But as we have mentioned in the first section, the separation frequencies vary a lot among separable VO compounds.

what would be the case if we consider the separation frequency: Is there a significant statistical correlation between the separation frequency and the transitivity frequency of the VOs?


However, extracting useful information for news writers from the vast amount of social media information is laborious.

services that enable news writers to extract important information are desired.


Information that news writers want to extract from social media includes many different topics such as fires, accidents, and other incidents.

extracting information from social media by filtering with keywords is difficult.


It has been reported that by using multi-task learning, the model can be generic and accurate (Luong et al., 2015b;Søgaard et al., 2016).

we also use multi-task learning so that our model is more accurate.


This table indicates that all methods outperformed query filtering, but the differences between each method are small.

we try another evaluation to find out the differences.


On the other hand, MeanVector attention gives weight without being affected much by the position of the character in the tweet.

meanVector attention gives high weight to " " (policemen) and " " (gathering).


On the other hand, in our "neural language model" training, losses are calculated for each input character, and these losses are much more than those of "target task" training.

"neural language model" training can help the model to learn more precisely for each input character.


Many viewers tweet while watching TV to share their opinions with other people.

many tweets include names of animations (which we call "virtual proper nouns") and TV programs.


The conventional method extracts information from tweets that include words related to incidents and accidents, regardless of whether one has actually occurred.

to utilize the extracted tweet as a news source, more work is required to determine whether it is a "real event" or "unreal event."


However, "Fire Bird" is the name of Japanese animation.

by adding proposed features, the proposed method can judge it as negative.


When the features were added, the positive and negative criteria were clarified.

our proposed method can maintain the diversity of the positive samples.


Instead, victims are promi-     nent when there is a particularly striking occurence within the case events -such as Emily Doe's letter in the Stanford rape case leading to the topic "rape event" and the words "victim", "woman" and "unconscious woman" being more prominent in the discourse; while the Vizconde Family's remembrance mass in the Vizconde massacre case lead to people discussing the topic "Vizconde family".

it can be said that victims have a time-dependent role in the conversation space.


Ky-WSD is open source software that can serve as the baseline system for a Japanese allwords WSD system.

it can be useful for several Japanese semantic analysis systems and is an advancement in all-words WSD technology.


We build KyWSD using KyTea because all-words WSD can be regarded as a kind of morphological analysis.

kyTea contains a mechanism for learning a model to adapt to a target domain.


For example, adding training data to KyWSD, senses to all words, but a target sense.

kyWSD is an appropriate system for domain adaptation.


Next, we translate test data to plain text and input it to KyWSD.

every words in the text is assigned its sense.


Moreover, as explained above, ignoring senses not in the sense list provided by that task, there are 6,986 correct senses for 8,953 answered instances.

the precision is 0.7803, and the F-value is improved to 0.7395.


the sense is added to the learning features.


That is, words with these two tones individually could occur in syllables with short or long nuclei (i.e., one or two vowels).

if the tones are not perceptually distinct and their contexts of occurrence are quite similar, is it possible that Tone 3 and Tone 4 are actually one tone rather than two separate tones?


Nevertheless, one caveat is warranted: (88)- 91are all examples of one specific structure (PST + N).

further studies should investigate other structures to see if the tone sandhi rule (87) applies throughout the phonology.


(2018) reported that a character-level NMT model trained on one Swiss-German dialect performed moderately well at translating sentences in closely-related dialects.

given this, we use multilingual NMT (Johnson et al., 2016) to learn parameters that encode knowledge of dialects' shared lexical and syntactic structure.


In our dataset, the dialect utterances have all been transcribed using kana; however, the standard Japanese translations use a mix of kanji and kana characters.

we converted these into kana sequences as well by automatically analyzing the pronunciation of each kanji character and replacing it with the corresponding kana sequence.


(2016) found that SMT models largely outperformed state-of-the-art NMT models for low-resource languages.

for comparison, the second row group in Table 2 shows results for a fixed-order character-based SMT baseline.


One limitation of the proposed model is it cannot consider longer-range dependencies beyond the chunk level.

our future research plans include incorporating contextual information into fixed-order translation models and investigating the dialect embeddings' characteristics further.


Models explored to achieve this involve a lot of manual tagging and checking of data.

there is a need to build a model that reduces human effort.


Additional features of the previous two tokens (words) and the follow-ing two tokens, i.e, a context window of size 5, are also extracted.

the final Hindi dataset consists of 'Root', 'Category', 'Gender', 'Person', 'Number', 'Case', 'Word', 'POS', 'Previous POS', 'Previous2 POS', 'Previous word', 'Previ-ous2 word', 'Next Word' and 'Next2 word' features.


Each data point can be tagged with a POS tag with a certain probability.

if you average the probabilities as scored by all models for a certain data point, it results in the average probability (AP) with which that data point could take on the corresponding tag.


Three models are picked from the HMM, CRF, SVM, LSTM, and the BiLSTM.

the features are given POS tag of the word, Model-1 probability, Model-2 probability, Model-3 probability.


Thus the features are given POS tag of the word, Model-1 probability, Model-2 probability, Model-3 probability.

the neural network trains on POS tagged data, and the probabilities with which a particular tag could be present, and is used to predict the presence of errors.


The LSTM and BiLSTM performed the best, with the LSTM was 98.22% accurate and the BiLSTM, 98.59% accurate.

hMM, SVM, LSTM, BiLSTM, and CRF++ were used for the ensemble model.


Voting Model 1: Using the sklearn library for SVM,the HMM, and the CRF, 1232 data points were tagged as errors (No errors were intentionally introduced into the data).

90.02% accuracy was achieved (Assuming no errors were present).


Note, the clause boundary labels are multilabel on the base phrase units, because more than one clause end boundary may appear within a base phrase.


When dealing with ambiguous words, it is desired to generate multi-sense embeddings, i.e., multiple representations per word.

several methods have been proposed to generate different word representations based on parts of speech or topic, but these methods tend to be too coarse to deal with ambiguity.


The simplest way to calculate the ratio is directly estimate P S (x) and P T (x), but in the case of complex models, the problem will be more complicated.

the method that directly models the probability density ratio was studied.


Therefore, (Shinnou and Sasaki, 2014) defined P r (x), the prior of x, as follows: , where x denotes a data in the domain R and x has a set of features, that is, They also obtain P R (f i ) using the following equation: .

here, n(R; f ) is the frequency of feature f in the domain R, and n(R) is the number of data in the domain R. the probability density ratio is obtained as follows: 3 Proposed Method The likelihood of the keyword in the target domain is l x , and l x is set as the value of IDF in the target domain of w: here N is the number of articles in the article collection in the target domain, and d i is the number of articles containing the word w in the article collection in the target domain.


Further, the combination is easy to implement in the neural network model.

we will investigate this approach in future.


In this paper, we propose to construct a semantic and logical representation of Arabic texts (the question and the passages of texts).

we propose a new approach for the Arabic question-answering.


In the existence of this large-scale digitization, finding high-precision information is a challenge.

the question-answering research is developing every day; Research in the field of Arabic question-answering began in the 1990s.


These systems provide answers in the form of short passages, extracts of document collections.

the performance of these systems is limited by the difficulty of the language processing and the considerable lack of effective NLP tools that take into consideration the Arabic language [Alagha & Abu-Taha, 2015], [Al-Khalifa & Al-Wabil, 2007].


The absence of voyellation in the Arabic texts is also a great source of ambiguity for morphological, syntactic and semantic analysis.

the Arabic language still lacks large-scale computing resources that are very useful for English as the WordNet [Miller, 1995], or a resource, such as, VerbOcean [Chklovski and Pantel, 2004].


Indeed, one of the great challenges in understanding the text, such as, constructing a coherent overall representation of the text, is that much of the information needed in this representation is implicit.

in order to fill the gaps and achieve a global representation, the language processing systems require a great deal of knowledge of the world, and the creation of these knowledge resources remains a fundamental challenge [Clark et al., 2008].


Otherwise, being able to represent a problem is interesting only if we can use this representation to perform intelligent tasks, such as, the logical reasoning.

another important role of the logic is to develop a mechanism that allows a machine to perform reasoning.


We suggest that automatic understanding of a text is one of the basic components of such a question-answering system for choosing and obtaining a precise answer.

our approach allows us to construct a semantic and logical representation of texts via the conceptual graphs.